During a process of manufacturing semiconductor products, it is concerned that short circuit may occur on a formed circuit pattern because foreign matter or the like is generated, or a defect such as breaking of wire, and a defect due to a problematic of conditions of a manufacturing process, and the like. In order to improve the product yield ratio, it is necessary to identify the root cause of such a defect at an early stage and to take countermeasures. For this purpose, it is necessary to inspect the semiconductor wafer for foreign matter adhered on a wafer surface and pattern defects formed on the wafer surface by using a device for inspecting foreign matter on semiconductor wafers or a visual inspection device for semiconductor wafers, and thereby continuously monitor occurrence of such defects and take measures to find the causes of such defects, based on inspection results.
Conventionally, such inspection has been carried out visually by a person. Accordingly, classification of detects of observation objects is biased, depending on an inspector. In order to solve this problem, in recent years, technologies for ADR (automatic defect review) and ADC (automatic defect classification), in which a device automatically performs determination of the size, the shape, the kind, and the like of a defect using an image processing technology, have come to be introduced. For example, in order to observe, in another word, review inspected parts (for example, patterns formed on wafers) by using an SEM (scanning electron microscopy) review device, a system that efficiently performs a task while reducing the workload of a user is proposed.
As a method for extracting information included in an inspection image as characteristic amounts and performing automatic classification based on the characteristic amounts, a method using a neural network is disclosed (for example, refer to Patent Document 1). Further, in order to reduce effects of inappropriate characteristic amounts on the classification performance in learning (weighting of respective characteristic amounts) for creating a classification standard for performing automatic classification, a method that automatically selects characteristic amounts that are effective for classification is disclosed (for example, refer to Patent Document 2).